updating

app.py

@@ -36,43 +36,38 @@ labels_cnn = {0: 'healthy', 1: 'patient'}
 labels_efficientnet = {0: 'healthy', 1: 'patient'}
 
 def classify_cnn(inp):
-    img = np.array(inp)
-    img = img.reshape((1, 224, 224, 3))
-    img = densenet.preprocess_input(img)
-    prediction = model_cnn.predict(img)
+    inp = inp.reshape((-1, 224, 224, 3))
+    inp = tf.keras.applications.densenet.preprocess_input(inp)
+    prediction = model_cnn.predict(inp)
     confidence = float(prediction[0])
     return {labels_cnn[prediction.argmax()]: confidence}
 
 def classify_efficientnet(inp):
-    img = np.array(inp)
-    img = img.reshape((1, 224, 224, 3))
-    img = efficientnet.preprocess_input(img)
-    prediction = model_efficientnet.predict(img)
+    inp = inp.reshape((-1, 224, 224, 3))
+    inp = tf.keras.applications.densenet.preprocess_input(inp)
+    prediction = model_efficientnet.predict(inp)
     confidence = float(prediction[0])
     return {labels_efficientnet[prediction.argmax()]: confidence}
+    
+# Create the Gradio interfaces for each binary classification model
+cnn_binary_interface = gr.Interface(fn=classify_cnn,
+                                    inputs=gr.inputs.Image(shape=(224, 224)),
+                                    outputs=gr.outputs.Label(num_top_classes=2),
+                                    title="CNN Binary Image Classification",
+                                    description="Classify an image as healthy or patient using a CNN model.",
+                                    examples=[['300104.png']])
 
-cnn_interface = gr.Interface(fn=classify_cnn, 
-                             inputs=gr.inputs.Image(shape=(224, 224)),
-                             outputs=gr.outputs.Label(num_top_classes=2),
-                             title="CNN Binary Image Classification",
-                             description="Classify an image as healthy or patient using a CNN model.",
-                             examples=[['300104.png']]
-                            )
+efficientnet_binary_interface = gr.Interface(fn=classify_efficientnet,
+                                             inputs=gr.inputs.Image(shape=(224, 224)),
+                                             outputs=gr.outputs.Label(num_top_classes=2),
+                                             title="EfficientNet Binary Image Classification",
+                                             description="Classify an image as healthy or patient using an EfficientNet model.",
+                                             examples=[['300104.png']])
 
-efficientnet_interface = gr.Interface(fn=classify_efficientnet, 
-                                       inputs=gr.inputs.Image(shape=(224, 224)),
-                                       outputs=gr.outputs.Label(num_top_classes=2),
-                                       title="EfficientNet Binary Image Classification",
-                                       description="Classify an image as healthy or patient using an EfficientNet model.",
-                                       examples=[['300104.png']]
-                                      )
-# create a combined interface with tabs for each binary classification model
-demo = gr.Interface([cnn_interface, efficientnet_interface], 
-                     "tab",
-                     title="Binary Image Classification",
-                     description="Classify an image as healthy or patient using different binary classification models."
-                    )
-
-demo.launch()
-             
+# Create the Gradio demo that allows the user to choose between the two binary classification models
+binary_demo = gr.Interface([cnn_binary_interface, efficientnet_binary_interface], 
+                            "tabs",
+                            title="Binary Image Classification",
+                            description="Classify an image as healthy or patient using one of two binary classification models.")
 
+binary_demo.launch()